{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T14:29:08Z","timestamp":1787754548673,"version":"build-2784847793"},"reference-count":36,"publisher":"Wiley","issue":"8","license":[{"start":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T00:00:00Z","timestamp":1785283200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T00:00:00Z","timestamp":1785283200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82260905"],"award-info":[{"award-number":["82260905"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004607","name":"Natural Science Foundation of Guangxi Province","doi-asserted-by":"publisher","award":["2023GXNSFAA026486"],"award-info":[{"award-number":["2023GXNSFAA026486"]}],"id":[{"id":"10.13039\/501100004607","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Clinical Cardiology"],"published-print":{"date-parts":[[2026,8]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Cardiovascular disease (CVD) risk assessment based on single\u2010time\u2010point measurements may inadequately capture the dynamic interplay of obesity, metabolic dysfunction, and chronic inflammation. Understanding their long\u2010term co\u2010evolution may improve CVD risk stratification.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We conducted a prospective cohort study using data from the China Health and Retirement Longitudinal Study. Participants aged\u2009\u2265\u200945 years with repeated measurements of body mass index (BMI), triglyceride\u2010glucose (TyG) index, and high\u2010sensitivity C\u2010reactive protein (hsCRP) between 2011 and 2015 were included. Joint trajectories of these obesity\u2010metabolic\u2010inflammatory markers were identified using the KmL3D clustering algorithm. Incident CVD (heart disease or stroke) was ascertained during follow\u2010up from 2015 to 2020. Cox proportional hazards models were applied to evaluate associations between trajectory groups and CVD risk after adjustment for demographic, lifestyle, and clinical covariates.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Among 4483 participants (mean age 57.8 years; 52.6% women), four distinct joint trajectory patterns were identified: a sustained low\u2010risk profile (46.8%), an obesity\u2010predominant profile (26.7%), a metabolic\u2013obesity\u2010driven profile (17.1%), and an inflammation\u2010dominant profile (9.4%). During a median follow\u2010up of 5.0 years, 592 incident CVD events occurred. Compared with the low\u2010risk profile, all adverse trajectory groups were associated with significantly higher CVD risk, with hazard ratios ranging from 1.35 to 1.46 after full adjustment. Associations were more pronounced for stroke, particularly among individuals with a metabolic\u2013obesity\u2010driven trajectory. Findings were robust across sensitivity analyses.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Distinct dynamic co\u2010evolution patterns of obesity, metabolic dysfunction, and inflammation identify population subgroups with substantially different risks of incident CVD.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/clc.70429","type":"journal-article","created":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T08:23:41Z","timestamp":1785313421000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Dynamic Co\u2010Evolution of Obesity\u2010Metabolic\u2010Inflammatory for Cardiovascular Disease Risk Stratification in Middle\u2010Aged and Older Adults: A Data\u2010Driven Joint Trajectory Analysis"],"prefix":"10.1002","volume":"49","author":[{"given":"Qinggao","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Cardiology The First Affiliated Hospital of Guangxi University of Chinese Medicine Nanning China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yilu","family":"Lei","sequence":"additional","affiliation":[{"name":"Geriatric Ward The First Affiliated Hospital of Guangxi University of Chinese Medicine Nanning China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingfeng","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Scientific Research The First Affiliated Hospital of Guangxi University of Chinese Medicine Nanning China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Long","sequence":"additional","affiliation":[{"name":"Geriatric Ward The First Affiliated Hospital of Guangxi University of Chinese Medicine Nanning China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-7429-2935","authenticated-orcid":false,"given":"Yinjuan","family":"Lai","sequence":"additional","affiliation":[{"name":"Department of Hospital Infection Management The First Affiliated Hospital of Guangxi University of Chinese Medicine Nanning 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